Machine learning-based economic development mapping from multi-source open geospatial data

Rui Cao, Wei Tu, Jixuan Cai, Tianhong Zhao, Jie Xiao, Jinzhou Cao, Qili Gao, Hanjing Su

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Timely and accurate socioeconomic indicators are the prerequisite for smart social governance. For example, the level of economic development and the structure of population are important statistics for regional or national policy-making. However, the collection of these characteristics usually depends on demographic and social surveys, which are time- and labor-intensive. To address these issues, we propose a machine learning-based approach to estimate and map the economic development from multi-source open available geospatial data, including remote sensing imagery and OpenStreetMap road networks. Specifically, we first extract knowledge-based features from different data sources; then the multi-view graphs are constructed through different perspectives of spatial adjacency and feature similarity; and a multi-view graph neural network (MVGNN) model is built on them and trained in a self-supervised learning manner. Then, the handcrafted features and the learned graph representations are combined to estimate the regional economic development indicators via random forest models. Taking China’s county-level gross domestic product (GDP) as an example, extensive experiments have been conducted and the results demonstrate the effectiveness of the proposed method, and the combination of the knowledge-based and learning-based features can significantly outperform baseline methods. Our proposed approach can advance the goal of acquiring timely and accurate socioeconomic variables through widely accessible geospatial data, which has the potential to extend to more social indicators and other geographic regions to support smart governance and policy-making in the future.
Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Pages259-266
Number of pages8
Volume5
Edition4
DOIs
Publication statusPublished - 17 May 2022

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus GmbH
ISSN (Print)2194-9042

Keywords

  • Data Fusion
  • Economic Development
  • Geospatial Big Data
  • Machine learning.
  • Remote Sensing

ASJC Scopus subject areas

  • Instrumentation
  • Environmental Science (miscellaneous)
  • Earth and Planetary Sciences (miscellaneous)

Cite this